US10348754B2

Data security incident correlation and dissemination system and method

Summary by NHIP

Correlated incident notification system

The system aggregates incident data from multiple organizations to generate correlated records and threat intelligence. It notifies affected entities when a given number of same-type correlations relate to given incident information to facilitate mitigation.

Claim Score by NHIP

Read claim 11, the broadest

Abstract

A data security incident correlation and dissemination system and method is disclosed. In an exemplary implementation of the system, a service provider of a managed security service receives incident information regarding data security incidents at different business organizations of the security service. One or more incident managers operated by different organizations send incident information, and a server system within the service provider's network creates aggregated data from the incident information received from the incident managers. The server system analyzes the aggregated data to create correlated incident records that include incident information from related data security incidents at the different organizations, and provides threat intelligence data based on the correlated incident records for the organizations. In embodiments, the server system can “push” threat intelligence data to the organizations, or the organizations can request the threat intelligence data from the server system.

US10348754B2, drawing sheet 1 — Sheet 1 of 6

Term

9.8 yearsleft in the term

Expires 28 July 2036, including 213 days of term adjustment.

20 claims: 2 independent, 18 dependent

1
A data security incident correlation and dissemination system, the system comprising:a server system that creates aggregated data from incident information received from a set of incident managers at least two of which are operated by different organizations, the server system including an analysis engine that analyzes the aggregated data to create correlated incident records that include incident information from data security incidents at the different organizations that have been determined by the analysis engine to be related, at least one correlated incident record being uniquely associated with a correlation found by the analysis engine and including a nature and relative strength of the correlation, and that provides threat intelligence data to the organizations based on the correlated incident records;wherein upon detecting that a given number of correlations of a same type within the correlated incident records has occurred and relate to given incident information, the server system is further operative to notify the different organizations that have been affected by the given incident to facilitate a mitigation effort.
11
Broadest claimClaim Score 52, average(NHIP)A data security dissemination method, the method comprising:receiving incident information from a set of incident managers at least two of which are operated by different organizations;and creating aggregated data from the incident information received from the incident managers, analyzing the aggregated data using an analysis engine to create correlated incident records that include incident information from data security incidents at the different organizations that have been determined by the analysis engine to be related, at least one correlated incident record being uniquely associated with a correlation found by the analysis engine and including a nature and relative strength of the correlation, and providing threat intelligence data based on the correlated incident records to the organizations;and responsive to detecting that a given number of correlations of a same type within the correlated incident records has occurred and relate to given incident information, notifying the different organizations that have been affected by the given incident to facilitate a mitigation effort.

Independent claims2

72 paragraphs in 4 sections, as filed

BACKGROUND OF THE INVENTION

Data security incidents pose a major operational and financial risk for organizations such as businesses and governments. In examples, data security incidents include loss of physical assets, such as a company laptop computer that contains company-confidential information and cyber attacks launched against systems and assets of a company's enterprise network. Any delays when responding to data security incidents such as cyber attacks increases the potential for damage to the organizations and loss of valuable data.

Cyber attacks target security vulnerabilities of computers within an organization's enterprise network. In examples, security vulnerabilities can exist in operating systems of the computers, within software executed by the operating systems, and within data networking systems of the enterprise network. Examples of cyber attacks include actions designed to disrupt normal business operations and intrusion attempts to obtain unauthorized access to the computer systems.

Increasingly, organizations are deploying incident manager (IM) applications to track responses to data security incidents such as cyber attacks.

SUMMARY OF THE INVENTION

Current incident manager (IM) applications will typically manage responses to data security incidents within specific organizations. Though data security incidents of the same type often occur across multiple organizations, current incident manager applications typically cannot disseminate or share responses to data security incidents between organizations such as different business entities. For example, one organization's responses to a Denial of Service (DoS) cyber attack launched in the recent past against a well-known TCP port cannot be applied to a similar cyber attack currently occurring within the enterprise network of a different organization. The effort associated with responding to the data security incident is effectively duplicated within the incident managers, which is inefficient.

The inability for incident managers of organizations to share responses to common data security incidents across different organizations wastes an opportunity to limit the response time and effectiveness of responding to future data security incidents of the same type, in one example. Organizations that are part of the same business sector are often targeted by the same cyber attacks. Sharing this information between organizations would potentially decrease the response time for responding to future cyber attacks for other organizations. An incident management system that enables incident manager applications of different organizations to share incident information of data security incidents and responses to the incidents can also be referred to as a data security incident correlation and dissemination system.

In general, according to one aspect, the invention features a data security incident correlation and/or dissemination system. The system comprises one or more incident managers operated by different organizations and a server system. The server system creates aggregated data from incident information received from the incident managers, analyzes the aggregated data to create correlated incident records that include incident information from related data security incidents at the different organizations, and provides threat intelligence data to the organizations based on the correlated incident records.

Preferably, the server system determines trends among the incident information within the aggregated data, and includes the trends as trends data within the threat intelligence data that the server system sends to the organizations. The server system also anonymizes the threat intelligence data to remove information that identifies the organizations. Typically, the incident information received from the incident managers of the organizations includes the incident information from the data security incidents at the organizations. This may include findings of significance such as indicators of compromise (e.g. hashes of files used by the attacker in carrying out the attack or other) or information about tactics, techniques and tools used by the attackers, in examples.

The incident information received from the incident managers also includes incident response data that includes recommendations and remediation techniques created by the organizations in response to the data security incidents at the organizations.

In one embodiment, the server system provides threat intelligence data to the organizations based on the correlated incident records by sending the threat intelligence data to the incident managers upon request by the incident managers.

The incident information received from the incident managers is typically pushed from the incident managers of the organizations.

The server system can receive sharing and usage information sent from the incident managers of the organizations that specifies rules for sharing the incident information with other organizations. The correlated incident records preferably include metadata that includes the sharing and usage information sent from the incident managers of the organizations.

In one example, the incident information can be used to identify organizations experiencing a similar threat, and to offer a mechanism to bring these organizations together to share information and collaborate on a response to the data security incidents.

According to a preferred embodiment, the server system includes predetermined rules and an analysis engine that compares the threat intelligence data to the predetermined rules, and wherein in response to events within the threat intelligence data matching the predetermined rules, the analysis engine sends the threat intelligence data associated with the matching events to the incident managers of the organizations.

In general, according to another aspect, the invention features a data security dissemination method. The method comprises one or more incident managers operated by different organizations sending incident information, and a server system creating aggregated data from the incident information received from the incident managers. The server system also analyzes the aggregated data to create correlated incident records that include incident information from related data security incidents at the different organizations, and provides threat intelligence data based on the correlated incident records for the organizations.

The above and other features of the invention including various novel details of construction and combinations of parts, and other advantages, will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular method and device embodying the invention are shown by way of illustration and not as a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings, reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale; emphasis has instead been placed upon illustrating the principles of the invention. Of the drawings:

<figref idref="DRAWINGS">FIG. 1</figref> shows a block diagram of an exemplary data security incident correlation and dissemination system that aggregates and correlates incident information concerning data security incidents from enterprise networks of selected organizations;

<figref idref="DRAWINGS">FIG. 2A</figref> is a flow chart that describes a method of operation of a server system of the data security incident correlation and dissemination system for responding to registration requests from incident managers of the organizations;

<figref idref="DRAWINGS">FIG. 2B</figref> is a flow chart that describes a method of operation of a preferred embodiment of the server system for creating aggregated data from incident information “pushed” from incident managers, and where the method also analyzes the aggregated data, derives and stores threat intelligence data from the analyzed aggregated data, and provides the threat intelligence data to the organizations via their incident managers;

<figref idref="DRAWINGS">FIG. 3</figref> and <figref idref="DRAWINGS">FIG. 4</figref> are flow charts that provides more detail for the flow chart of <figref idref="DRAWINGS">FIG. 2B</figref>; and

<figref idref="DRAWINGS">FIGS. 5A and 5B</figref> are flow charts that show an alternate embodiment of the server system, with <figref idref="DRAWINGS">FIG. 5A</figref> showing how the server system responds to a request from an incident manager of an organization for threat intelligence data associated with a specific incident, and with <figref idref="DRAWINGS">FIG. 5B</figref> showing how the server system responds to a request from an incident manager of an organization for all trends data of the threat intelligence data over a specific time period.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The invention now will be described more fully hereinafter with reference to the accompanying drawings, in which illustrative embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Further, the singular forms and the articles “a”, “an” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms: includes, comprises, including and/or comprising, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, it will be understood that when an element, including component or subsystem, is referred to and/or shown as being connected or coupled to another element, it can be directly connected or coupled to the other element or intervening elements may be present.

<figref idref="DRAWINGS">FIG. 1</figref> shows an exemplary implementation of a data security incident correlation and dissemination system 10. The system 10 includes a service provider network 132 of a service provider that provides a managed security service to one or more business organizations. The service provider network 132 communicates over a network cloud 26 with one or more enterprise networks 130 of the organizations. In one example, the service provider network 132 and one or more of the organizations are all owned or operated by different business entities.

Enterprise networks 130-1 through 130-4 of exemplary organizations ACME Company, BigCorp, CamCorp, and DataCorp are shown, respectively. Firewalls 36-1 through 36-4 enable communications between corporate networks 70-1 through 70-4 of enterprise networks 130-1 through 130-4, respectively.

The service provider network 132 includes a server system 190 and a datastore 135. Also shown are one or more threat information sources (TIS) 40. Examples include malware, geolocation, and domain name threat information sources 40-1 through 40-3, respectively.

The server system 190 executes queries associated with data security incidents against one or more threat information sources (TIS) 40 to obtain more information about the incidents. The server system 190 saves information based on the data security incidents to the datastore 135. In one example, the server system 190 is an expert system that includes predictive technologies such as artificial intelligence and machine learning capabilities.

The server system 190 includes an application server 180-1, IM registration data 94 and an analysis engine 176. The server system 190 also includes predetermined rules 177, aggregated data 46 and a notifier 96.

Each organization operates one or more incident managers 102 that manage data security incidents detected within the enterprise networks 130 of the organizations. In one implementation, the incident manager(s) 102 are physically located within the service provider network 132 and are implemented as SaaS applications hosted by application server 180-1. In another implementation, the incident manager(s) (102-1) are physically located within the respective corporate networks 70-1. In the illustrated example, incident managers 102-2 and 102-3 manage data security incidents for organization BigCorp's Finance and Manufacturing subnets 72 of BigCorp's enterprise network 130-2, and incident manager 102-4 manages data security incidents for organization DataCorp's enterprise network 130-4. In another implementation, the incident managers 102 are included within the enterprise networks 130 of the organizations themselves. For example, incident manager 102-1 for ACME Company is hosted by application server 180-2 within ACME Company's enterprise network 130-1 and incident manager 102-5 for CamCorp is hosted by application server 180-3 within CamCorp's enterprise network 130-3.

The components within ACME Company's enterprise network 130-1 and that of its incident manager 102-1 are a typical example of components within organizations that communicate with the service provider network 132. The enterprise network 130-1 for ACME Company includes exemplary devices, software and systems that communicate over a corporate network 70 and among one or more subnetworks 72. The subnetworks 72 are segmented from the corporate network 70 via a router 34. Devices include firewall 36-1, router 34, web server 160, and a configuration server 103. The web server 160 includes a browser 150, which incident response team (IRT) 172 personnel utilize to manage and configure the devices and the incident manager 102-1.

ACME Company's incident manager 102-1 includes a knowledge base 122 and a messaging system 62. The knowledge base 122 stores information associated with data security incidents detected within ACME Company's enterprise network 130-1. The knowledge base 122 includes sharing and usage information 144-1, action conditions 44, and incident information 59. The incident information 59 includes details of the incidents such as incident objects 41, incident artifacts (IAs) 120 and notes 121. The incident information 59 also includes recommended actions and remediation techniques published by the organizations in response to specific incidents, also known as incident response data 92.

When ACME Company's incident manager 102-1 detects that incident information 59 has satisfied/met conditions of the action conditions 44, the incident manager 102-1 includes the incident information 59 in messages. The incident manager 102-1 then sends the messages to its messaging system 62. The messages can include incident information 59 and sharing and usage information 144-1, in examples. The messaging system 62 is implemented as a Java Messaging Service (JMS), in one example. The messaging system 62 can either “push” the messages over the network 26 to the server system 190, or alternatively, the server system 190 can poll or request the messaging systems 62 of each of the incident managers 102 for the messages, in examples.

When ACME Company's incident manager 102-1 includes incident information 59 in the messages sent to the server system 102, the incident manager 102-1 can additionally include sharing and usage information 144-1 in the messages. The sharing and usage information 144-1 is associated with the incident information 59 and indicates whether the incident information 59 can be shared with other organizations. The sharing and usage information 144-1 specifies rules and/or policies (e.g. access control lists) for disseminating the incident information 59 among the organizations. The server system 190 receives the messages from the incident managers 102 of the organizations, and uses the sharing and usage information 144 in the received messages as the basis for subsequent sharing and usage policy in data that the server system 190 creates in response to receiving the messages.

The notifier 96 receives the messages from the incident managers 102 and creates aggregated data 46 from the messages. Each record of aggregated data 46 includes incident information 59 and optionally sharing and usage information 144-2. The analysis engine 176 can also process the messages received by the notifier 96 and create IM registration data 94 that includes sharing and usage information 144-3.

The server system 190 stores threat intelligence data 54 to datastore 135. Threat intelligence data 54 includes correlated incident records 58 and trends data 50. In a preferred embodiment, the server system 190 via its notifier 96 receives incident information 59 and sharing and usage information 144 that are pushed from each of the incident managers 102. In an alternate implementation, the server system 190 via its notifier 96 can request or poll the incident managers 102 for the incident information 59 and sharing and usage information 144.

The analysis engine 176 analyzes the records of aggregated data 46 to determine correlations or similarities among the records of aggregated data 46. For each correlation found, the analysis engine 176 creates a separate correlated incident record 58 that includes the incident information 59 from the aggregated data 46 records determined to have correlations or similarities. The sharing and usage information 144-5 associated with the incident information 59 is stored within metadata 56 of each correlated incident record 58.

The analysis engine 176 then determines trends among the incident information 59 within the correlated incident records 58, and includes the trends as trends data 50 within the threat intelligence data. If the correlated incident records 58 include sharing and usage information 144-5 in its metadata 56, the analysis engine includes the sharing and usage information 144-6 within the trends data 50.

In examples, the capabilities provided by the server system 190 can be implemented by a single physical computer system or be distributed across multiple physical or virtual computer systems. In the former example, the server system 190 is a single physical computer system located within the service provider network 132 of the service provider. In the latter example, the server system 190 includes multiple physical computer systems located within the service provider network 132 and one or more virtual computer systems, where the virtual computer systems are third party cloud based services that the service provider accesses via the network cloud 26. In this way, processing associated with the aggregation, correlation, and dissemination of information based on incident information 59 of data security incidents can be shared or distributed across the one or more physical or virtual computer systems of the notional server system 190.

<figref idref="DRAWINGS">FIG. 2A</figref> shows a registration method of the server system 190. The registration specifies the scope of the information sharing to which each organization allows.

In step 196, the notifier 96 receives a registration request from one or more incident managers 102, where the registration request specifies the organizations that each incident manager will share its incident information 59 with, and where the registration request also specifies the scope of the information usage and sharing between the organizations. In step 198, the notifier 96 stores the information in the registration request to IM registration data 94 of the server system 190.

<figref idref="DRAWINGS">FIG. 2B</figref> describes a method of operation of the server system for creating aggregated data 46 for incidents and creating threat intelligence data 54.

In step 202, the server system 190 waits for the next message from an incident manager 102 within an enterprise network 130. In step 204, the Notifier 96 receives incident information 59 (e.g. incident objects 41, incident artifacts 120, notes 121, incident response data 92) associated with data security incidents included within messages “pushed” from incident managers 102 of organizations, where the messages optionally include sharing and usage information 144 for the incident information 59.

According to step 206, Notifier 96 formats the received incident information 59 within the messages into aggregated data 46, where the aggregated data 46 additionally includes optional sharing and usage information 144 for the incident information 59.

In step 208, the notifier 96 tests if any sharing and usage information 144 was included within the messages sent by the incident managers 102. If the result of this test is true, the method transitions to step 214. Otherwise, the method transitions to step 210.

In step 210, the notifier 96 executes a lookup within the IM registration data 94 to determine sharing and usage information 144-3 for the incident manager 102 sending the message. In step 212, the notifier 96 tests if an entry for the current incident manager 102 is found within the IM registration data 94. If no entry is found, the method transitions back to step 202 to wait for more messages sent from incident managers 102. Otherwise, the method transitions to step 214.

In step 214, the analysis engine 176 analyzes the aggregated data 46, derives threat intelligence data 54 from the analysis, and stores anonymized threat intelligence data 54 to a datastore 135, where the threat intelligence data 54 is subsequently available upon request by incident managers 102 of different member organizations.

Upon completion of step 214, the method transitions to step 216. In step 216, in response to events within the threat intelligence data 54 matching predetermined rules 177, the analysis engine sends threat intelligence data 54 associated with the matching events to one or more incident managers 102 of the organizations.

<figref idref="DRAWINGS">FIG. 3</figref> is a flow chart that provides more detail for step 214 of <figref idref="DRAWINGS">FIG. 2B</figref>.

In step 302, the analysis engine 176 analyzes the incident information 59 of the aggregated data 46 to determine trends among the incident information 59. The analysis engine 176 anonymizes the determined trends, and stores the determined trends in conjunction with the sharing and usage information 144 to trends data 50 of the threat intelligence data 54 within the datastore 135. Preferably, the trends data 50 is stored in a format for providing to incident managers 102 upon request by the incident managers 102.

In an alternate implementation, the analysis engine 176 can “push” the trends data 50 to the incident managers 102 of the organizations in an unsolicited fashion. Regardless of the method in which the trends data 50 is provided by the server system 190 to the incident managers 102, the included sharing and usage information 144-6 specifies rules and/or the policies for disseminating the trends data 50 among the organizations.

According to step 306, the analysis engine 176 searches the aggregated data 46 for correlations among the incident information 59 sent from one or more incident managers 102, within the scope agreed upon by the organizations. This sharing scope is provided via either the sharing and information 144-2 included within the aggregated data 46 or within the sharing and information 144-3 included within the IM registration data 94.

Then, in step 308, the analysis engine 176 determines if any correlations between incident information 59 were found among the aggregated data 46. If the result of this test is true, the method transitions to step 310 to continue processing. Otherwise, the method transitions to step 324 to end processing.

In step 310, for each set of correlated incident information 59 found in the aggregated data 46, the analysis engine 176 executes a lookup against the contents of any currently stored correlated incident records 58 in accordance with its usage and sharing information 144-5. If any correlations were found between incident information 59 of aggregated data 46 and incident information 59 of any stored correlated incident records 58, the method transitions to step 314. Otherwise, the method transitions to step 318.

In step 314, the analysis engine 176 updates the correlated incident records 58 to include the correlated incident information determined from the aggregated data 46. Then, in step 316, the analysis engine 176 updates metadata 56 of the correlated incident records 58 with information regarding the nature and relative strength of the correlations and with any sharing and usage information 144-2 associated with the correlated incident information 59.

In step 318, the analysis engine 176 creates and stores one or more correlated incident records 58 that include the correlated incident information determined from the aggregated data 46. In step 320, the analysis engine 176 stores information regarding the nature and relative strength of the correlations and any sharing and usage information 144-2 associated with the correlated incident information 59 to correlation metadata 46 within the correlated incident records 58.

Upon completion of both step 316 and 320, the method transitions to step 322.

In step 322, the analysis engine 176 enriches the incident artifacts 120 and/or other incident information 59 of the correlated incident records 58 by executing lookups of incident artifact 120 details against other Threat Information Sources 40 (e.g. convert an IP address data resource of an incident artifact 120 to a DNS name via a lookup against the Domain Name TIS 40-3, in one example). The analysis engine 176 can also append information obtained from the lookup against the threat information sources 40 to the incident artifacts 120 as another way of enriching the incident artifacts 120.

Upon conclusion of step 322, the threat intelligence data 54 is available for incident managers 102 to request. In examples, the incident managers 102 can request the entirety of the threat intelligence data 54, or only subsets of the threat intelligence data 54. In one example, multiple incident managers 102 request only the incident response data records 92 within the incident information 59 of the correlated incident records 58. In the example, IRT 172 personnel can provide narrowing information in the request message such as the IP address of a “known bad” incident artifact 120, with the intention of obtaining all incident response data 92 that other organizations published in response to incidents having the same IP address in their incident artifact(s) 120 and that the server system 190 included in one or more correlated incident records 58. Preferably, the incident response data 92 is stored in a format for providing to incident managers 102 upon request by the incident managers 102.

In response, the server system 190 sends associated incident response data 92 only to those incident managers 102 of the organizations that are authorized to view the incident response data 92. The server system 190 uses the sharing and usage policy information 144-5 stored within the metadata 56 of the correlated incident record 58 from which each incident response data 192 was extracted to authorize the transaction. The method then transitions to step 324 to end processing.

<figref idref="DRAWINGS">FIG. 4</figref> is a flow chart that provides more detail for step 216 of <figref idref="DRAWINGS">FIG. 2B</figref>. The method of <figref idref="DRAWINGS">FIG. 4</figref> provides examples of how the analysis engine 176 of the server system 190 analyzes the threat intelligence data 54, and “pushes” relevant portions of the threat intelligence data 54 matching predetermined rules 177 to the incident managers 102 of the organizations.

In step 402, in accordance with the sharing and usage information 144-6 within the trends data 50, the analysis engine 176 provides trends data 50 matching predetermined rules 177 to the incident managers 102, e.g. “the most active artifact over the last 24 hrs is X”, or “organizations in the Finance sector are seeing a 27% increase in DDoS incidents over the last 6 hrs,” in examples.

According to step 404, in accordance with the sharing and usage information 144-6 within the trends data 50, the analysis engine 176 provides specific information to incident managers 102 associated with contents of correlated incident records 58 matching the predetermined rules 177, e.g. that incident artifacts 120 of incident information 59 within correlated incident records 58 are “known bad” artifacts as well as providing suggested correlations. An example of a “known bad” incident artifact 120 is one that includes data resources such as IP addresses associated with known malware websites and sources.

In step 406, in response to a predetermined rule 177 that specifies detecting increasing strength of correlations within the correlated incident records 58, the analysis engine 176 enables users to view other attributes of strongly correlated incidents of the correlated incident records 58, such as proposed remediation steps within the incident response data 92, shared analyst notes 121, attributed threat actors and other indicators of compromise, in examples.

According to step 408, in response to a predetermined rule 177 that specifies detecting an increasing number of correlations of the same type within the correlated incident records 59, the analysis engine 176 enables users to request to be connected to other individuals working those other incidents or to join pre-existing groups of individuals working on correlated incidents. For example, upon analyzing the incident information 59 in the correlated incident records 58, the analysis engine 176 may determine that a predetermined rule 177 associated with an increasing number of “malware” incidents from the same source IP address is met. In response, the analysis engine 176 can send invitations to the organizations affected by the common malware correlated incident records 59 to participate in a phone conference to share their experiences and propose solutions.

Finally, in step 410, the analysis engine 176 enables users of the system 10 to rate information from other correlated incidents (e.g. via “thumbs up or thumbs down”) which will affect their overall rating and save ratings to metadata 56 of the correlated incident records 59. The analysis engine 176 enables this by sending a link (e.g. URL) for a ratings/voting website to the affected organizations, in another example.

<figref idref="DRAWINGS">FIG. 5A</figref> shows a method for an exemplary request issued by an incident manager 102 to the server system 190, for threat intelligence data 54 matching a specific set of requested incident information 59.

In step 502, the notifier 96 of the server system 190 receives a query message from an incident manager 102, where the query message includes a list of incident information 59 of potentially different types. Then, in step 504, the notifier 96 executes a lookup of the incident information 59 in the received message against the threat intelligence data 54 stored on the datastore 135 to obtain any stored correlated incident records 58 having incident information 59 matching the requested incident information 59. In step 510, the notifier 96 determines if any matching correlated incident records 58 were found. If this statement is true, the method transitions to step 512. Otherwise, the method terminates at step 516.

In step 512, the notifier 96 filters the matching correlated incident records 58 in accordance with the sharing and usage information 144-5 included within the metadata 56 of each matching correlated incident record 58. The notifier 96, in step 514, sends a reply message to the requesting incident manger 102 that includes contents of the correlated incident records 58 not discarded from the filter operation executed in step 512. The method then terminates at step 516.

<figref idref="DRAWINGS">FIG. 5B</figref> shows a method for an exemplary request issued by an incident manager 102 to the server system 190, for trends data 50 of the threat intelligence data 54 over a specified time period.

In step 530, the notifier 96 receives a query message from an incident manager 102 to obtain trends data 50 for a specified time period (e.g. within last 24 hours). Then, in step 532, the notifier 96 executes a lookup of the threat intelligence data 54 for its trends data 50, for the specified time period. In step 534, the notifier 96 determines if any trends data 50 for the specified time period were found. If this statement is true, the method transitions to step 536 to continue processing. Otherwise, the method terminates at step 540.

In step 536, the notifier 96 filters the trends data 50 in accordance with the sharing and usage information 144-6 within the trends data 50. According to step 538, the notifier 96 sends a reply message to the requesting incident manager 102, where the reply message includes contents of the trends data 50 not discarded from the filter operation executed in step 536. The method terminates at step 540.

While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.

Contents4

Every citation, both waysCites: the store holds 43 of 44

Citations
Document	Relation	Office	Cited during
US2007180490A1	Cites	United States of America	Search report
US2010325685A1	Cites	United States of America	Search report
US2013124223A1	Cites	United States of America	Applicant
US2013332590A1	Cites	United States of America	Applicant
US2014278664A1	Cites	United States of America	Applicant
US2014304822A1	Cites	United States of America	Applicant
US2015113663A1	Cites	United States of America	Applicant
US2015235164A1	Cites	United States of America	Applicant
US2015242625A1	Cites	United States of America	Applicant
US2015244681A1	Cites	United States of America	Search report
US2016021133A1	Cites	United States of America	Applicant
US2016072836A1	Cites	United States of America	Applicant
US2016127394A1	Cites	United States of America	Applicant
US6678827B1	Cites	United States of America	Applicant
US7376969B1	Cites	United States of America	Search report
US7877804B2	Cites	United States of America	Applicant
US8032557B1	Cites	United States of America	Applicant
US8244777B1	Cites	United States of America	Applicant
US8510446B1	Cites	United States of America	Search report
US8661062B1	Cites	United States of America	Applicant
US8707445B2	Cites	United States of America	Applicant
US8763133B2	Cites	United States of America	Applicant
US8782784B1	Cites	United States of America	Applicant
US8880682B2	Cites	United States of America	Applicant
US9069930B1	Cites	United States of America	Applicant
US9075668B1	Cites	United States of America	Applicant
US9083734B1	Cites	United States of America	Applicant
US9152706B1	Cites	United States of America	Applicant
US9215270B2	Cites	United States of America	Applicant
US9258321B2	Cites	United States of America	Applicant
US20070180490A1	Cites	United States of America	Search report
US20100325685A1	Cites	United States of America	Search report
US20130124223A1	Cites	United States of America	Applicant
US20130332590A1	Cites	United States of America	Applicant
US20140278664A1	Cites	United States of America	Applicant
US20140304822A1	Cites	United States of America	Applicant
US20150113663A1	Cites	United States of America	Applicant
US20150235164A1	Cites	United States of America	Applicant
US20150242625A1	Cites	United States of America	Applicant
US20150244681A1	Cites	United States of America	Search report
US20160021133A1	Cites	United States of America	Applicant
US20160072836A1	Cites	United States of America	Applicant
US20160127394A1	Cites	United States of America	Applicant
Arbor Networks Peakflow X_eng_0, NCERT-Lab-Pubdoc-2011-12-003, Version 1.00, Croatian Academic and Research Network. Twenty-six pages (2011).	Non-patent	–	Applicant
BlackStratus SIEMStorm, “Rapidly identify and resolve threats, . . . ” www.blackstratus.com, 2012. Four pages.	Non-patent	–	Applicant
Domino Project Management, “Track and Control Projects with Lotus Notes,” www.trackersuite.com/index, 2013. Two pages.	Non-patent	–	Applicant
HP ArcSight Express, “World-Class Protection for the Mid-Size Organization,” www.arcsight.com, 2010. Six pages.	Non-patent	–	Applicant
Janet Csirt, “RTIR incident handling work-flow,” Janet (UK) WI/JCSIRT/002, jisc.ac.uk, 2011. Eighteen pages.	Non-patent	–	Applicant
Jarocki, J., “Orion Incident Response Live CD,” 2010, Sans Institute, https://www.sans.org. Forty-five pages.	Non-patent	–	Applicant
Khurana, H. et al., “Palantir: A Framework for Collaborative Incident Response and Investigation,” IDtrust, 2009. Fourteen pages.	Non-patent	–	Applicant
QRadar Administration Guide, http://www.q1labs.com, May 2012. 318 pages.	Non-patent	–	Applicant
QRadar Users Guide, http://www.q1labs.com, May 2012. 396 pages.	Non-patent	–	Applicant
Reddy, K. et al., “The architecture of a digital forensic readiness management system,” Computers & Security 32 (2013) 73-89. Seventeen pages.	Non-patent	–	Applicant
Swift, D., “A Practical Application of SIM/SEM/SIEM Automating Threat Identification,” 2006, SANS Institute 2007. Forty-one pages.	Non-patent	–	Applicant
West-Brown, M. et al., “Handbook for Computer Security Incident Response Teams (CSIRTs),” 2nd Edition, Apr. 2003. 223 pages.	Non-patent	–	Applicant
U.S. Appl. No. 14/839,304, filed Aug. 28, 2015, entitled “Incident Response Bus for Data Security Incidents.” Specification and drawings, twenty-four pages.	Non-patent	–	Applicant
Arbor Networks Peakflow X_eng_0, NCERT-Lab-Pubdoc-2011-12-003, Version 1.00, Croatian Academic and Research Network. Twenty-six pages (2011).	Non-patent	–	Applicant
BlackStratus SIEMStorm, “Rapidly identify and resolve threats, . . . ” www.blackstratus.com, 2012. Four pages.	Non-patent	–	Applicant
Domino Project Management, “Track and Control Projects with Lotus Notes,” www.trackersuite.com/index, 2013. Two pages.	Non-patent	–	Applicant
HP ArcSight Express, “World-Class Protection for the Mid-Size Organization,” www.arcsight.com, 2010. Six pages.	Non-patent	–	Applicant
Janet Csirt, “RTIR incident handling work-flow,” Janet (UK) WI/JCSIRT/002, jisc.ac.uk, 2011. Eighteen pages.	Non-patent	–	Applicant
Jarocki, J., “Orion Incident Response Live CD,” 2010, Sans Institute, https://www.sans.org. Forty-five pages.	Non-patent	–	Applicant
Khurana, H. et al., “Palantir: A Framework for Collaborative Incident Response and Investigation,” IDtrust, 2009. Fourteen pages.	Non-patent	–	Applicant
QRadar Administration Guide, http://www.q1labs.com, May 2012. 318 pages.	Non-patent	–	Applicant
QRadar Users Guide, http://www.q1labs.com, May 2012. 396 pages.	Non-patent	–	Applicant
Reddy, K. et al., “The architecture of a digital forensic readiness management system,” Computers & Security 32 (2013) 73-89. Seventeen pages.	Non-patent	–	Applicant
Swift, D., “A Practical Application of SIM/SEM/SIEM Automating Threat Identification,” 2006, SANS Institute 2007. Forty-one pages.	Non-patent	–	Applicant
West-Brown, M. et al., “Handbook for Computer Security Incident Response Teams (CSIRTs),” 2nd Edition, Apr. 2003. 223 pages.	Non-patent	–	Applicant
U.S. Appl. No. 14/839,304, filed Aug. 28, 2015, entitled “Incident Response Bus for Data Security Incidents.” Specification and drawings, twenty-four pages.	Non-patent	–	Applicant

2 members in 1 office

This recordMember, by publication datePriority claim

Priority claims2

Priority claims
Document	Office	Kind	Date
201514981266	United States of America	A	2015-12-28
US201514981266	–	–	–

Members2

Family members, oldest first
Document	Office	Kind
US2017187742A1	United States of America	A1	2017-06-29
US10348754B2This record	United States of America	B2	2019-07-09

82 transactions on the USPTO file

Allowed after 2 non-final rejections, 1 final rejection and 1 RCE.

Non-final rejections: 2
Final rejections: 1
RCEs: 1
Appeals: 0

Over time

Point at a mark for the transaction

Transactions

Prosecution transactions, newest first
	Event	Code
2024-11-01	Correspondence Address ChangeC.ADB	C.ADB
2023-01-10	Surcharge for Late Payment, Large EntityM1554	M1554
2023-01-10	Payment of Maintenance Fee, 4th Year, Large EntityM1551	M1551
2023-01-05	Entity Status Set To Undiscounted (Initial Default Setting or Status Change)BIG.	BIG.
2022-03-07	Email NotificationEML_NTR	EML_NTR
2022-03-07	Change in Power of Attorney (May Include Associate POA)PA..	PA..
2022-03-06	Correspondence Address ChangeC.AD	C.AD
2019-07-09	Recordation of Patent Grant MailedPGM/	PGM/
2019-07-09	Patent Issue Date Used in PTA CalculationAllowedPTAC	PTAC
2019-06-20	Email NotificationEML_NTR	EML_NTR
2019-06-19	Issue Notification MailedAllowedWPIR	WPIR
2019-06-11	Dispatch to FDCD1935	D1935
2019-06-03	Application Is Considered Ready for IssuePILS	PILS
2019-06-03	Correspondence Address ChangeC.AD	C.AD
2019-05-24	Issue Fee Payment VerifiedN084	N084
2019-05-24	Issue Fee Payment ReceivedIFEE	IFEE
2019-03-02	Electronic ReviewELC_RVW	ELC_RVW
2019-02-25	Email NotificationEML_NTF	EML_NTF
2019-02-25	Mail Notice of AllowanceAllowedMN/=.	MN/=.
2019-02-21	Notice of Allowance Data Verification CompletedAllowedN/=.	N/=.
2019-02-19	Reasons for AllowanceEX.R	EX.R
2019-02-01	Date Forwarded to ExaminerFWDX	FWDX
2019-01-07	Response after Non-Final ActionA...	A...
2018-10-05	Electronic ReviewELC_RVW	ELC_RVW
2018-10-05	Email NotificationEML_NTF	EML_NTF
2018-10-05	Mail Non-Final RejectionNon-final rejectionMCTNF	MCTNF
2018-10-01	Non-Final RejectionNon-final rejectionCTNF	CTNF
2018-09-13	Disposal for a RCE / CPA / R129AbandonedABN9	ABN9
2018-09-13	Date Forwarded to ExaminerFWDX	FWDX
2018-09-10	Request for Continued Examination (RCE)RCEX	RCEX
2018-09-10	Request for Extension of Time - GrantedXT/G	XT/G
2018-09-10	Workflow - Request for RCE - BeginBRCE	BRCE
2018-05-16	Electronic ReviewELC_RVW	ELC_RVW
2018-05-09	Email NotificationEML_NTF	EML_NTF
2018-05-09	Mail Final Rejection (PTOL - 326)Final rejectionMCTFR	MCTFR
2018-04-28	Final RejectionFinal rejectionCTFR	CTFR
2018-01-26	Date Forwarded to ExaminerFWDX	FWDX
2018-01-08	Response after Non-Final ActionA...	A...
2017-10-11	Email NotificationEML_NTR	EML_NTR
2017-10-11	Email NotificationEML_NTR	EML_NTR
2017-10-11	Filing Receipt - CorrectedFLRCPT.C	FLRCPT.C
2017-10-11	Change in Power of Attorney (May Include Associate POA)PA..	PA..
2017-10-10	Correspondence Address ChangeC.AD	C.AD
2017-10-06	Electronic ReviewELC_RVW	ELC_RVW
2017-10-06	Email NotificationEML_NTF	EML_NTF
2017-10-06	Mail Non-Final RejectionNon-final rejectionMCTNF	MCTNF
2017-10-02	Non-Final RejectionNon-final rejectionCTNF	CTNF
2017-10-02	Information Disclosure Statement consideredIDSC	IDSC
2017-10-02	Information Disclosure Statement consideredIDSC	IDSC
2017-10-02	Information Disclosure Statement consideredIDSC	IDSC
2017-09-18	Electronic ReviewELC_RVW	ELC_RVW
2017-09-18	Email NotificationEML_NTF	EML_NTF
2017-09-18	Mail Pre-Exam NoticeMPEN	MPEN
2017-07-04	Case Docketed to Examiner in GAUDOCK	DOCK
2017-06-30	Email NotificationEML_NTR	EML_NTR
2017-06-29	Application ready for PDX access by participating foreign officesCCRDY	CCRDY
2017-06-29	PG-Pub Issue NotificationPG-ISSUE	PG-ISSUE
2016-09-23	Email NotificationEML_NTR	EML_NTR
2016-09-23	Change in Power of Attorney (May Include Associate POA)PA..	PA..
2016-09-09	Electronic Information Disclosure StatementEIDS.	EIDS.
2016-09-09	Information Disclosure Statement (IDS) FiledWIDS	WIDS
2016-07-12	Electronic Information Disclosure StatementEIDS.	EIDS.
2016-07-12	Electronic Information Disclosure StatementEIDS.	EIDS.
2016-07-12	Information Disclosure Statement (IDS) FiledWIDS	WIDS
2016-05-13	Case Docketed to Examiner in GAUDOCK	DOCK
2016-05-05	Application Dispatched from OIPEOIPE	OIPE
2016-02-22	Electronic Information Disclosure StatementEIDS.	EIDS.
2016-02-22	Information Disclosure Statement (IDS) FiledWIDS	WIDS
2016-01-14	Email NotificationEML_NTR	EML_NTR
2016-01-14	Application Is Now CompleteCOMP	COMP
2016-01-14	Filing ReceiptFLRCPT.O	FLRCPT.O
2016-01-14	Application Is Now CompleteCOMP	COMP
2016-01-13	Sent to Classification ContractorPGPC	PGPC
2016-01-13	FITF set to YES - revise initial settingFTFS	FTFS
2016-01-12	Applicant Has Filed a Verified Statement of Small Entity Status in Compliance with 37 CFR 1.27SMAL	SMAL
2016-01-03	Cleared by OIPE CSRL194	L194
2015-12-30	IFW Scan & PACR Auto Security ReviewSCAN	SCAN
2015-12-28	Patent Term Adjustment - Ready for ExaminationPTA.RFE	PTA.RFE
2015-12-28	Applicants have given acceptable permission for participating foreignAPPERMS	APPERMS
2015-12-28	PTO/SB/69-Authorize EPO Access to Search ResultsSREXR141	SREXR141
2015-12-28	Entity Status Set To Undiscounted (Initial Default Setting or Status Change)BIG.	BIG.
2015-12-28	Initial Exam Team nnIEXX	IEXX

15 legal events, as the office reported them to INPADOC

Over the term

Point at a mark for the event

Events

Legal events, newest first
	Event	Code
2024-06-21	AssignmentAS	AS
2024-06-21	AssignmentAS	AS
2024-05-29	AssignmentAS	AS
2024-05-29	AssignmentAS	AS
2023-01-10	Fee payment procedureSURCHARGE FOR LATE PAYMENT, LARGE ENTITY (ORIGINAL EVENT CODE: M1554); ENTITY STATUS OF PATENT OWNER: LARGE ENTITYFEPP	FEPP
2023-01-10	Maintenance fee paymentMAFP	MAFP
2023-01-05	Fee payment procedureENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITYFEPP	FEPP
2022-01-27	AssignmentAS	AS
2019-06-19	Information on status: patent grantGrantedPATENTED CASESTCF	STCF
2019-06-03	Information on status: patent application and granting procedure in generalPUBLICATIONS -- ISSUE FEE PAYMENT VERIFIEDSTPP	STPP
2019-05-24	Information on status: patent application and granting procedure in generalPUBLICATIONS -- ISSUE FEE PAYMENT RECEIVEDSTPP	STPP
2019-02-21	Information on status: patent application and granting procedure in generalNOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONSSTPP	STPP
2017-01-13	AssignmentAS	AS
2017-01-13	AssignmentAS	AS
2016-09-14	AssignmentAS	AS